Machine learning surrogates for efficient hydrologic modeling: Insights from stochastic simulations of managed aquifer recharge

Autor: Dai, Timothy, Maher, Kate, Perzan, Zach

Process-based hydrologic models are invaluable tools for understanding the terrestrial water cycle and addressing modern water resources problems. However, many hydrologic models are computationally expensive and, depending on the resolution and scal

Externí odkaz: http://arxiv.org/abs/2407.20902

Simulation of anoxic lenses as exporters of reactivity in alluvial aquifer sediments

Autor: Babey, Tristan, Boye, Kristin, Tolar, Bradley, Engel, Maya, Noël, Vincent, Perzan, Zach, Kumar, Naresh, Francis, Christopher A., Bargar, John R., Maher, Kate

Publikováno v: In Geochimica et Cosmochimica Acta 1 October 2022 334:119-134

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Interpreting Parameter Interactions using Global Sensitivity Analysis on a Hillslope-scale Reactive Transport Model

Autor: Perzan, Zach, Babey, Tristan, Maher, Katharine

Publikováno v: American Geophysical Union Fall Meeting 2020 (AGU 2020)
American Geophysical Union Fall Meeting 2020 (AGU 2020), Dec 2020, Online, United States. pp.H036-0010, 2020
American Geophysical Union Fall Meeting 2020 (AGU 2020), Dec 2020, Online, United States., pp.H036-0010, 2020

International audience; Reactive transport models (RTMs) are valuable tools for understanding, interrogating and forecasting subsurface processes, but the complex interplay between intricate flow paths and simultaneous reactions can make them difficu

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::9fc415598ea5318f4ece20a9896d0dde
https://hal-insu.archives-ouvertes.fr/insu-03222745

Sample Identifiers and Metadata to Support Data Management and Reuse in Multidisciplinary Ecosystem Sciences.

Autor: DAMEROW, JOAN E., VARADHARAJAN, CHARULEKA, BOYE, KRISTIN, BRODIE, EOIN L., BURRUS, MADISON, CHADWICK, K. DANA, CRYSTAL-ORNELAS, ROBERT, ELBASHANDY, HESHAM, ALVES, RICARDO J. ELOY, ELY, KIM S., GOLDMAN, AMY E., HABERMAN, TED, HENDRIX, VALERIE, KAKALIA, ZARINE, KEMNER, KENNETH M., KERSTING, ANNIE B., MERINO, NANCY, O'BRIEN, FIANNA, PERZAN, ZACH, ROBLES, EMILY

Publikováno v: Data Science Journal; 2021, Vol. 20, p1-19, 19p